An Energy Box in a Cloud-Based Architecture for Autonomous Demand Response of Prosumers and Prosumages

Brusco, Giovanni; Burgio, Alessandro; Menniti, Daniele; Pinnarelli, Anna; Sorrentino, N.; Scarcello, Luigi

doi:10.3390/electronics6040098

Cited by 20 publications

(14 citation statements)

References 21 publications

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“…In this way, the converters respond not only to the level of the DC bus voltage, but each converter is able to control indirectly the other converters connected to the same DC bus. The SEB includes a 7-inch touch-screen, a Raspberry Pi3 a Tapko KNX sim, and a user-friendly interface through which the user sets his/her preferences regarding the schedulable load and the thermal comfort [33]. It communicates with the CEP and asks for services such as the hourly electricity prices and the hourly PV generation forecast.…”

Section: Resultsmentioning

confidence: 99%

An Energy Community Implementation: The Unical Energy Cloud

Giordano¹,

Mastroianni²,

Menniti

et al. 2019

Electronics

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Reducing greenhouse gas emissions, limiting the effects of climate change and decreasing the environmental, social and economic costs of energy production are some of the main issues related to the sustainable development of modern society. Energy communities, envisioned to enable local energy exchange between consumers and producers of renewable energy, represent a possible scenario towards a cleaner and sustainable energy system. In this paper, an energy community management model called Power Cloud and presented in previous papers is proposed for a real-world practical application at the University of Calabria. In particular, the implementation of the information and communication technology (ICT) architecture and other enabling technologies, such as the nanogrid and the smart energy box, are discussed in detail. The experiment results show that by adopting the Power Cloud management model it is possible to obtain significant savings in terms of energy cost, which provide benefit for a community, such as a university campus.

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Section: Resultsmentioning

confidence: 99%

An Energy Community Implementation: The Unical Energy Cloud

Giordano¹,

Mastroianni²,

Menniti

et al. 2019

Electronics

Self Cite

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“…Therefore, the concept of DR has a strong potential for helping consumers to decrease their electricity bill by reducing the energy consumption during peak periods and is essential for the optimal operation of HEMS [24]. The implementation of an effective DR process is a vital element during the design of HEMS [25]. For this reason the study of this concept has drawn a lot of attention from the research community [26].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

et al. 2018

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Abstract:The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.

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“…Such tools are embedded in data monitoring applications, such as innovative web-based energy management platforms [23,24] to enable improved analysis, decision making, and dynamic controls. Moving from Building Energy Management Systems (BEMS) [25,26] to District Energy Management Systems (DEMS) [27] entails the dynamic exchange and hierarchical processing of data streams between various components and systems, as in the Internet of Things (IoT) paradigm [28,29]. Various techniques and tools have been investigated for dealing with challenges in various fields pertaining to smart grids: smart metering data analysis and dynamic processing [30], power demand forecasting [31,32], Distributed Energy Resources (DER) management optimisation [33], users' engagement [34], etc.…”

Section: Introductionmentioning

confidence: 99%

Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

et al. 2018

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Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessary open and transparent market framework linking energy costs to the actual grid operations. DR allows consumers to directly or indirectly participate in the markets where energy is being exchanged. One of the main challenges for engaging in DR is associated with the initial assessment of the potential rewards and risks under a given pricing scheme. In this paper, a Genetic Algorithm (GA) optimisation model, using Artificial Neural Network (ΑΝΝ) power predictions for day-ahead energy management at the building and district levels, is proposed. Individual building and building group analysis is conducted to evaluate ANN predictions and GA-generated solutions. ANN-based short term electric power forecasting is exploited in predicting day-ahead demand, and form a baseline scenario. GA optimisation is conducted to provide balanced load shifting and cost-of-energy solutions based on two alternate pricing schemes. Results demonstrate the effectiveness of this approach for assessing DR load shifting options based on a Time of Use pricing scheme. Through the analysis of the results, the practical benefits and limitations of the proposed approach are addressed.

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An Energy Box in a Cloud-Based Architecture for Autonomous Demand Response of Prosumers and Prosumages

Cited by 20 publications

References 21 publications

An Energy Community Implementation: The Unical Energy Cloud

An Energy Community Implementation: The Unical Energy Cloud

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

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